Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x1d4a7467d30>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x1d4a752b2b0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input = tf.placeholder(tf.float32,(None,image_height,image_width,image_channels),name='real_input')
    z_input = tf.placeholder(tf.float32,(None,z_dim),name='z_input')
    lr_rate = tf.placeholder(tf.float32,name='lr_rate') 
    
    
    return real_input, z_input, lr_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator',reuse=reuse):
        #input_shape is 28*28*3
        conv1 = tf.layers.conv2d(images,64,5,strides=2,padding='same')
        relu1 = tf.maximum(conv1*0.01,conv1)
        #14*14*64
        conv2 = tf.layers.conv2d(relu1,128,5,strides=2,padding='same')
        bn2 = tf.layers.batch_normalization(conv2,training=True)
        relu2 = tf.maximum(bn2*0.01,conv2)
        #7*7*128
        conv3 = tf.layers.conv2d(relu2,256,3,strides=1,padding='same')
        bn3 = tf.layers.batch_normalization(conv3,training=True)
        relu3 = tf.maximum(bn3*0.01,conv3)
        #7*7*256
        flatten = tf.reshape(relu3,(-1,7*7*256))
        logits = tf.layers.dense(flatten,1)
        output = tf.sigmoid(logits)
        
    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator',reuse=not is_train):
        x1 = tf.layers.dense(z,7*7*512)
        x1 = tf.reshape(x1,(-1,7,7,512))
        x1 = tf.layers.batch_normalization(x1,training=is_train)
        x1 = tf.maximum(x1*0.01,x1)
        
        x2 = tf.layers.conv2d_transpose(x1,256,3,strides=1,padding='same')
        x2 = tf.layers.batch_normalization(x2,training=is_train)
        x2 = tf.maximum(x2*0.01,x2)
        
        x3 = tf.layers.conv2d_transpose(x2,128,5,strides=2,padding='same')
        x3 = tf.layers.batch_normalization(x3,training=is_train)
        x3 = tf.maximum(x3*0.01,x3)
        
        logits = tf.layers.conv2d_transpose(x3,out_channel_dim,5,strides=2,padding='same')

        output = tf.tanh(logits)
        
        
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z,out_channel_dim)
    d_model_real,d_logits_real = discriminator(input_real)
    d_model_fake,d_logits_fake = discriminator(g_model,reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_logits_real),logits=d_logits_real))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_logits_fake),logits=d_logits_fake))
    d_loss = d_loss_real + d_loss_fake
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_logits_fake),logits=d_logits_fake))
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [34]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
  
    d_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate,beta1=beta1).minimize(d_loss,var_list=d_vars)
    g_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate,beta1=beta1).minimize(g_loss,var_list=g_vars)
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [35]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [38]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    real_input,z_input,lr_rate = model_inputs(data_shape[1],data_shape[2],data_shape[3],z_dim)
    d_loss,g_loss = model_loss(real_input,z_input,data_shape[-1])
    d_opt,g_opt = model_opt(d_loss,g_loss,learning_rate,beta1)
    


    samples, losses = [], []
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                # TODO: Train Mode
#                 print(batch_images.shape)
                batch_z = np.random.uniform(-1,1,size=(batch_size,z_dim))
#                 print('images:',batch_images)
#                 print('\nbatch_z: ',batch_z)
#                 print('\nlearning_rate: ',learning_rate)
                _ = sess.run(d_opt,{z_input:batch_z,lr_rate:learning_rate,real_input:batch_images})
                _ = sess.run(g_opt,{z_input:batch_z,lr_rate:learning_rate})
        
                if steps % 20 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({z_input: batch_z, real_input: batch_images})
                    train_loss_g = g_loss.eval({z_input: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))

                if steps % 20 == 0:
                    show_generator_output(sess,4,z_input,data_shape[-1],data_image_mode)

                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [39]:
batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.9915... Generator Loss: 0.9045
Epoch 1/2... Discriminator Loss: 1.1331... Generator Loss: 0.9860
Epoch 1/2... Discriminator Loss: 1.3094... Generator Loss: 0.6579
Epoch 1/2... Discriminator Loss: 0.8735... Generator Loss: 1.5657
Epoch 1/2... Discriminator Loss: 0.7387... Generator Loss: 1.4923
Epoch 1/2... Discriminator Loss: 0.4704... Generator Loss: 1.7932
Epoch 1/2... Discriminator Loss: 0.9955... Generator Loss: 2.5074
Epoch 1/2... Discriminator Loss: 0.2658... Generator Loss: 3.2888
Epoch 1/2... Discriminator Loss: 9.7743... Generator Loss: 2.4560
Epoch 1/2... Discriminator Loss: 0.4549... Generator Loss: 1.9148
Epoch 1/2... Discriminator Loss: 1.2826... Generator Loss: 0.5679
Epoch 1/2... Discriminator Loss: 0.6880... Generator Loss: 1.4059
Epoch 1/2... Discriminator Loss: 0.7966... Generator Loss: 0.7666
Epoch 1/2... Discriminator Loss: 1.3351... Generator Loss: 0.5386
Epoch 1/2... Discriminator Loss: 0.6869... Generator Loss: 1.3120
Epoch 1/2... Discriminator Loss: 0.7376... Generator Loss: 1.6930
Epoch 1/2... Discriminator Loss: 0.5512... Generator Loss: 2.0910
Epoch 1/2... Discriminator Loss: 0.2940... Generator Loss: 3.1206
Epoch 1/2... Discriminator Loss: 0.4485... Generator Loss: 1.5813
Epoch 1/2... Discriminator Loss: 1.2808... Generator Loss: 1.6715
Epoch 1/2... Discriminator Loss: 0.5374... Generator Loss: 1.3118
Epoch 1/2... Discriminator Loss: 0.4782... Generator Loss: 3.8994
Epoch 1/2... Discriminator Loss: 0.6358... Generator Loss: 1.4640
Epoch 1/2... Discriminator Loss: 0.2413... Generator Loss: 3.2740
Epoch 1/2... Discriminator Loss: 0.7101... Generator Loss: 1.2010
Epoch 1/2... Discriminator Loss: 0.3309... Generator Loss: 2.2365
Epoch 1/2... Discriminator Loss: 0.1424... Generator Loss: 4.6729
Epoch 1/2... Discriminator Loss: 0.3533... Generator Loss: 2.0599
Epoch 1/2... Discriminator Loss: 0.4262... Generator Loss: 2.3770
Epoch 1/2... Discriminator Loss: 1.0287... Generator Loss: 0.6085
Epoch 1/2... Discriminator Loss: 0.5972... Generator Loss: 3.8044
Epoch 1/2... Discriminator Loss: 1.0870... Generator Loss: 1.0825
Epoch 1/2... Discriminator Loss: 0.6965... Generator Loss: 1.6754
Epoch 1/2... Discriminator Loss: 0.5683... Generator Loss: 1.1789
Epoch 1/2... Discriminator Loss: 0.5845... Generator Loss: 1.1017
Epoch 1/2... Discriminator Loss: 1.0767... Generator Loss: 0.7397
Epoch 1/2... Discriminator Loss: 0.7105... Generator Loss: 1.3601
Epoch 1/2... Discriminator Loss: 0.5247... Generator Loss: 1.5929
Epoch 1/2... Discriminator Loss: 0.3676... Generator Loss: 2.0714
Epoch 1/2... Discriminator Loss: 1.0000... Generator Loss: 0.6405
Epoch 1/2... Discriminator Loss: 0.1730... Generator Loss: 4.9675
Epoch 1/2... Discriminator Loss: 0.7879... Generator Loss: 1.1837
Epoch 1/2... Discriminator Loss: 0.5139... Generator Loss: 1.3868
Epoch 1/2... Discriminator Loss: 0.8288... Generator Loss: 0.7799
Epoch 1/2... Discriminator Loss: 0.9382... Generator Loss: 5.1550
Epoch 1/2... Discriminator Loss: 0.2737... Generator Loss: 2.4479
Epoch 1/2... Discriminator Loss: 0.2726... Generator Loss: 2.4310
Epoch 1/2... Discriminator Loss: 0.9013... Generator Loss: 1.0020
Epoch 1/2... Discriminator Loss: 0.9209... Generator Loss: 4.9255
Epoch 1/2... Discriminator Loss: 0.3955... Generator Loss: 1.6128
Epoch 1/2... Discriminator Loss: 3.1462... Generator Loss: 2.0627
Epoch 1/2... Discriminator Loss: 0.8107... Generator Loss: 1.1220
Epoch 1/2... Discriminator Loss: 0.7291... Generator Loss: 2.2043
Epoch 1/2... Discriminator Loss: 0.6952... Generator Loss: 1.2260
Epoch 1/2... Discriminator Loss: 0.4942... Generator Loss: 3.8248
Epoch 1/2... Discriminator Loss: 0.5502... Generator Loss: 1.1735
Epoch 1/2... Discriminator Loss: 0.3000... Generator Loss: 4.0515
Epoch 1/2... Discriminator Loss: 0.3449... Generator Loss: 3.7334
Epoch 1/2... Discriminator Loss: 2.0731... Generator Loss: 0.6800
Epoch 1/2... Discriminator Loss: 0.7237... Generator Loss: 1.1620
Epoch 1/2... Discriminator Loss: 0.6288... Generator Loss: 1.7244
Epoch 1/2... Discriminator Loss: 1.4441... Generator Loss: 0.3487
Epoch 1/2... Discriminator Loss: 0.6965... Generator Loss: 5.0429
Epoch 1/2... Discriminator Loss: 0.4754... Generator Loss: 1.5076
Epoch 1/2... Discriminator Loss: 2.3868... Generator Loss: 0.1481
Epoch 1/2... Discriminator Loss: 0.9608... Generator Loss: 0.6579
Epoch 1/2... Discriminator Loss: 0.1266... Generator Loss: 4.4591
Epoch 1/2... Discriminator Loss: 1.1471... Generator Loss: 0.7433
Epoch 1/2... Discriminator Loss: 0.5180... Generator Loss: 1.7207
Epoch 1/2... Discriminator Loss: 0.4852... Generator Loss: 1.4733
Epoch 1/2... Discriminator Loss: 0.6669... Generator Loss: 1.4168
Epoch 1/2... Discriminator Loss: 1.0389... Generator Loss: 1.0375
Epoch 1/2... Discriminator Loss: 0.7532... Generator Loss: 1.4329
Epoch 1/2... Discriminator Loss: 0.7058... Generator Loss: 1.0322
Epoch 1/2... Discriminator Loss: 0.3950... Generator Loss: 1.5984
Epoch 1/2... Discriminator Loss: 0.9528... Generator Loss: 0.6964
Epoch 1/2... Discriminator Loss: 0.6311... Generator Loss: 1.3506
Epoch 1/2... Discriminator Loss: 0.1151... Generator Loss: 3.9274
Epoch 1/2... Discriminator Loss: 1.4402... Generator Loss: 0.5950
Epoch 1/2... Discriminator Loss: 1.0915... Generator Loss: 0.5519
Epoch 1/2... Discriminator Loss: 0.3415... Generator Loss: 2.3581
Epoch 1/2... Discriminator Loss: 0.9198... Generator Loss: 2.5261
Epoch 1/2... Discriminator Loss: 0.7212... Generator Loss: 0.9931
Epoch 1/2... Discriminator Loss: 0.6111... Generator Loss: 1.6389
Epoch 1/2... Discriminator Loss: 1.4144... Generator Loss: 0.3815
Epoch 1/2... Discriminator Loss: 1.7489... Generator Loss: 0.3482
Epoch 1/2... Discriminator Loss: 0.3288... Generator Loss: 3.1826
Epoch 1/2... Discriminator Loss: 0.1843... Generator Loss: 2.2586
Epoch 1/2... Discriminator Loss: 0.1042... Generator Loss: 3.4225
Epoch 1/2... Discriminator Loss: 0.0822... Generator Loss: 4.1862
Epoch 1/2... Discriminator Loss: 0.4307... Generator Loss: 1.7301
Epoch 1/2... Discriminator Loss: 2.3364... Generator Loss: 0.2691
Epoch 1/2... Discriminator Loss: 1.7307... Generator Loss: 0.3567
Epoch 2/2... Discriminator Loss: 0.6366... Generator Loss: 1.2881
Epoch 2/2... Discriminator Loss: 0.6479... Generator Loss: 1.6791
Epoch 2/2... Discriminator Loss: 0.5079... Generator Loss: 1.6073
Epoch 2/2... Discriminator Loss: 0.7285... Generator Loss: 1.3783
Epoch 2/2... Discriminator Loss: 0.7455... Generator Loss: 1.7205
Epoch 2/2... Discriminator Loss: 0.9788... Generator Loss: 0.7458
Epoch 2/2... Discriminator Loss: 0.3491... Generator Loss: 2.2219
Epoch 2/2... Discriminator Loss: 0.4694... Generator Loss: 1.3379
Epoch 2/2... Discriminator Loss: 0.5318... Generator Loss: 1.3042
Epoch 2/2... Discriminator Loss: 0.9087... Generator Loss: 0.9535
Epoch 2/2... Discriminator Loss: 1.2834... Generator Loss: 0.5673
Epoch 2/2... Discriminator Loss: 0.5581... Generator Loss: 2.1803
Epoch 2/2... Discriminator Loss: 0.8085... Generator Loss: 0.8907
Epoch 2/2... Discriminator Loss: 0.4410... Generator Loss: 1.9616
Epoch 2/2... Discriminator Loss: 1.2955... Generator Loss: 0.5187
Epoch 2/2... Discriminator Loss: 1.2012... Generator Loss: 0.5867
Epoch 2/2... Discriminator Loss: 0.1585... Generator Loss: 2.6412
Epoch 2/2... Discriminator Loss: 0.0542... Generator Loss: 4.8594
Epoch 2/2... Discriminator Loss: 2.2190... Generator Loss: 0.2071
Epoch 2/2... Discriminator Loss: 0.9589... Generator Loss: 0.8140
Epoch 2/2... Discriminator Loss: 0.2631... Generator Loss: 3.3766
Epoch 2/2... Discriminator Loss: 0.1797... Generator Loss: 2.6538
Epoch 2/2... Discriminator Loss: 0.5746... Generator Loss: 1.6373
Epoch 2/2... Discriminator Loss: 0.6434... Generator Loss: 1.4041
Epoch 2/2... Discriminator Loss: 0.7545... Generator Loss: 0.9833
Epoch 2/2... Discriminator Loss: 0.7370... Generator Loss: 1.0333
Epoch 2/2... Discriminator Loss: 0.7270... Generator Loss: 1.1756
Epoch 2/2... Discriminator Loss: 0.7380... Generator Loss: 1.6367
Epoch 2/2... Discriminator Loss: 0.8440... Generator Loss: 1.8391
Epoch 2/2... Discriminator Loss: 0.8513... Generator Loss: 0.8838
Epoch 2/2... Discriminator Loss: 0.6115... Generator Loss: 1.8802
Epoch 2/2... Discriminator Loss: 1.0389... Generator Loss: 0.8479
Epoch 2/2... Discriminator Loss: 0.8896... Generator Loss: 2.7914
Epoch 2/2... Discriminator Loss: 0.3750... Generator Loss: 1.7420
Epoch 2/2... Discriminator Loss: 0.9228... Generator Loss: 0.9701
Epoch 2/2... Discriminator Loss: 1.3609... Generator Loss: 0.5246
Epoch 2/2... Discriminator Loss: 0.5535... Generator Loss: 1.2431
Epoch 2/2... Discriminator Loss: 0.0559... Generator Loss: 4.4247
Epoch 2/2... Discriminator Loss: 0.4597... Generator Loss: 1.9997
Epoch 2/2... Discriminator Loss: 0.6397... Generator Loss: 1.7656
Epoch 2/2... Discriminator Loss: 0.5840... Generator Loss: 2.3186
Epoch 2/2... Discriminator Loss: 0.7689... Generator Loss: 1.2147
Epoch 2/2... Discriminator Loss: 1.0625... Generator Loss: 0.8831
Epoch 2/2... Discriminator Loss: 0.5619... Generator Loss: 1.3068
Epoch 2/2... Discriminator Loss: 0.8267... Generator Loss: 0.9991
Epoch 2/2... Discriminator Loss: 0.5869... Generator Loss: 2.9453
Epoch 2/2... Discriminator Loss: 0.2602... Generator Loss: 2.5543
Epoch 2/2... Discriminator Loss: 0.5265... Generator Loss: 1.4627
Epoch 2/2... Discriminator Loss: 0.6169... Generator Loss: 1.4961
Epoch 2/2... Discriminator Loss: 0.9143... Generator Loss: 0.8298
Epoch 2/2... Discriminator Loss: 0.2972... Generator Loss: 2.2057
Epoch 2/2... Discriminator Loss: 0.1881... Generator Loss: 2.8200
Epoch 2/2... Discriminator Loss: 2.3812... Generator Loss: 0.1397
Epoch 2/2... Discriminator Loss: 0.8156... Generator Loss: 3.5789
Epoch 2/2... Discriminator Loss: 0.7182... Generator Loss: 1.2183
Epoch 2/2... Discriminator Loss: 0.4208... Generator Loss: 1.9680
Epoch 2/2... Discriminator Loss: 0.7750... Generator Loss: 1.0046
Epoch 2/2... Discriminator Loss: 0.5882... Generator Loss: 1.4471
Epoch 2/2... Discriminator Loss: 0.9320... Generator Loss: 0.8129
Epoch 2/2... Discriminator Loss: 0.5298... Generator Loss: 1.5508
Epoch 2/2... Discriminator Loss: 0.3189... Generator Loss: 1.8513
Epoch 2/2... Discriminator Loss: 0.8593... Generator Loss: 0.9890
Epoch 2/2... Discriminator Loss: 0.6326... Generator Loss: 1.8046
Epoch 2/2... Discriminator Loss: 0.8572... Generator Loss: 1.0710
Epoch 2/2... Discriminator Loss: 0.5881... Generator Loss: 1.7560
Epoch 2/2... Discriminator Loss: 0.7110... Generator Loss: 1.4719
Epoch 2/2... Discriminator Loss: 0.6577... Generator Loss: 1.6215
Epoch 2/2... Discriminator Loss: 1.2216... Generator Loss: 3.2972
Epoch 2/2... Discriminator Loss: 0.5161... Generator Loss: 1.8246
Epoch 2/2... Discriminator Loss: 0.6208... Generator Loss: 2.1467
Epoch 2/2... Discriminator Loss: 0.8409... Generator Loss: 1.0959
Epoch 2/2... Discriminator Loss: 1.5389... Generator Loss: 0.4378
Epoch 2/2... Discriminator Loss: 0.5482... Generator Loss: 1.6895
Epoch 2/2... Discriminator Loss: 0.3496... Generator Loss: 2.2156
Epoch 2/2... Discriminator Loss: 0.6390... Generator Loss: 1.9269
Epoch 2/2... Discriminator Loss: 0.7714... Generator Loss: 1.0020
Epoch 2/2... Discriminator Loss: 0.8823... Generator Loss: 0.8799
Epoch 2/2... Discriminator Loss: 1.0805... Generator Loss: 0.7035
Epoch 2/2... Discriminator Loss: 0.4030... Generator Loss: 1.8742
Epoch 2/2... Discriminator Loss: 0.7918... Generator Loss: 1.1062
Epoch 2/2... Discriminator Loss: 0.5986... Generator Loss: 1.3125
Epoch 2/2... Discriminator Loss: 0.6339... Generator Loss: 1.4242
Epoch 2/2... Discriminator Loss: 0.1029... Generator Loss: 4.2723
Epoch 2/2... Discriminator Loss: 0.1575... Generator Loss: 3.4735
Epoch 2/2... Discriminator Loss: 1.0291... Generator Loss: 2.7925
Epoch 2/2... Discriminator Loss: 0.6382... Generator Loss: 1.1454
Epoch 2/2... Discriminator Loss: 0.9609... Generator Loss: 0.7149
Epoch 2/2... Discriminator Loss: 1.2871... Generator Loss: 0.5976
Epoch 2/2... Discriminator Loss: 0.5526... Generator Loss: 1.9402
Epoch 2/2... Discriminator Loss: 1.1336... Generator Loss: 0.7060
Epoch 2/2... Discriminator Loss: 0.6008... Generator Loss: 1.2087
Epoch 2/2... Discriminator Loss: 0.1137... Generator Loss: 3.8085
Epoch 2/2... Discriminator Loss: 0.5645... Generator Loss: 1.2383
Epoch 2/2... Discriminator Loss: 0.2405... Generator Loss: 2.4007

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [40]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.1995... Generator Loss: 0.4368
Epoch 1/1... Discriminator Loss: 1.2352... Generator Loss: 1.1892
Epoch 1/1... Discriminator Loss: 1.1394... Generator Loss: 0.9626
Epoch 1/1... Discriminator Loss: 0.9267... Generator Loss: 1.0147
Epoch 1/1... Discriminator Loss: 1.4943... Generator Loss: 2.4542
Epoch 1/1... Discriminator Loss: 1.7208... Generator Loss: 0.2472
Epoch 1/1... Discriminator Loss: 0.8293... Generator Loss: 1.3567
Epoch 1/1... Discriminator Loss: 0.8958... Generator Loss: 1.4454
Epoch 1/1... Discriminator Loss: 1.5649... Generator Loss: 1.3389
Epoch 1/1... Discriminator Loss: 0.9006... Generator Loss: 1.5787
Epoch 1/1... Discriminator Loss: 1.1456... Generator Loss: 1.0143
Epoch 1/1... Discriminator Loss: 1.1291... Generator Loss: 0.7500
Epoch 1/1... Discriminator Loss: 1.3827... Generator Loss: 0.7364
Epoch 1/1... Discriminator Loss: 1.1489... Generator Loss: 0.8348
Epoch 1/1... Discriminator Loss: 1.2432... Generator Loss: 0.6756
Epoch 1/1... Discriminator Loss: 1.1999... Generator Loss: 0.7836
Epoch 1/1... Discriminator Loss: 1.3787... Generator Loss: 1.1844
Epoch 1/1... Discriminator Loss: 1.1317... Generator Loss: 0.9619
Epoch 1/1... Discriminator Loss: 1.2220... Generator Loss: 0.9673
Epoch 1/1... Discriminator Loss: 1.1929... Generator Loss: 0.7082
Epoch 1/1... Discriminator Loss: 1.1520... Generator Loss: 0.8186
Epoch 1/1... Discriminator Loss: 1.0775... Generator Loss: 0.7757
Epoch 1/1... Discriminator Loss: 1.1336... Generator Loss: 0.7704
Epoch 1/1... Discriminator Loss: 1.2883... Generator Loss: 0.5261
Epoch 1/1... Discriminator Loss: 1.8392... Generator Loss: 0.2804
Epoch 1/1... Discriminator Loss: 1.1965... Generator Loss: 0.7120
Epoch 1/1... Discriminator Loss: 1.2254... Generator Loss: 0.7466
Epoch 1/1... Discriminator Loss: 2.0908... Generator Loss: 0.1782
Epoch 1/1... Discriminator Loss: 1.5807... Generator Loss: 1.3487
Epoch 1/1... Discriminator Loss: 1.1908... Generator Loss: 1.0421
Epoch 1/1... Discriminator Loss: 1.5299... Generator Loss: 0.5012
Epoch 1/1... Discriminator Loss: 1.3384... Generator Loss: 0.8306
Epoch 1/1... Discriminator Loss: 0.9086... Generator Loss: 1.0746
Epoch 1/1... Discriminator Loss: 1.0256... Generator Loss: 1.1514
Epoch 1/1... Discriminator Loss: 1.4109... Generator Loss: 0.6321
Epoch 1/1... Discriminator Loss: 1.0254... Generator Loss: 1.0535
Epoch 1/1... Discriminator Loss: 1.1539... Generator Loss: 0.7794
Epoch 1/1... Discriminator Loss: 1.2960... Generator Loss: 0.7432
Epoch 1/1... Discriminator Loss: 1.4293... Generator Loss: 0.9098
Epoch 1/1... Discriminator Loss: 1.3817... Generator Loss: 0.6998
Epoch 1/1... Discriminator Loss: 1.4986... Generator Loss: 1.4095
Epoch 1/1... Discriminator Loss: 1.2222... Generator Loss: 0.8830
Epoch 1/1... Discriminator Loss: 1.2997... Generator Loss: 0.5895
Epoch 1/1... Discriminator Loss: 1.1151... Generator Loss: 0.9104
Epoch 1/1... Discriminator Loss: 1.2167... Generator Loss: 0.6626
Epoch 1/1... Discriminator Loss: 1.0413... Generator Loss: 1.0066
Epoch 1/1... Discriminator Loss: 1.1703... Generator Loss: 0.7123
Epoch 1/1... Discriminator Loss: 1.2365... Generator Loss: 0.6732
Epoch 1/1... Discriminator Loss: 1.5485... Generator Loss: 0.3726
Epoch 1/1... Discriminator Loss: 1.2151... Generator Loss: 0.8075
Epoch 1/1... Discriminator Loss: 1.3922... Generator Loss: 0.7255
Epoch 1/1... Discriminator Loss: 1.4051... Generator Loss: 0.6133
Epoch 1/1... Discriminator Loss: 1.3975... Generator Loss: 0.7068
Epoch 1/1... Discriminator Loss: 1.1635... Generator Loss: 0.7321
Epoch 1/1... Discriminator Loss: 1.4931... Generator Loss: 0.5174
Epoch 1/1... Discriminator Loss: 1.2050... Generator Loss: 0.7447
Epoch 1/1... Discriminator Loss: 1.4085... Generator Loss: 0.5487
Epoch 1/1... Discriminator Loss: 1.4137... Generator Loss: 0.6142
Epoch 1/1... Discriminator Loss: 1.2229... Generator Loss: 0.7055
Epoch 1/1... Discriminator Loss: 1.2372... Generator Loss: 0.7882
Epoch 1/1... Discriminator Loss: 1.4105... Generator Loss: 0.6122
Epoch 1/1... Discriminator Loss: 1.0550... Generator Loss: 0.8867
Epoch 1/1... Discriminator Loss: 1.3432... Generator Loss: 0.9205
Epoch 1/1... Discriminator Loss: 1.3198... Generator Loss: 0.6186
Epoch 1/1... Discriminator Loss: 1.2590... Generator Loss: 0.7046
Epoch 1/1... Discriminator Loss: 1.2558... Generator Loss: 0.7749
Epoch 1/1... Discriminator Loss: 1.3274... Generator Loss: 0.7026
Epoch 1/1... Discriminator Loss: 1.4771... Generator Loss: 0.5503
Epoch 1/1... Discriminator Loss: 1.7098... Generator Loss: 0.5087
Epoch 1/1... Discriminator Loss: 1.3105... Generator Loss: 0.7002
Epoch 1/1... Discriminator Loss: 1.2198... Generator Loss: 0.8077
Epoch 1/1... Discriminator Loss: 1.2503... Generator Loss: 0.5985
Epoch 1/1... Discriminator Loss: 1.5216... Generator Loss: 0.7386
Epoch 1/1... Discriminator Loss: 1.3482... Generator Loss: 0.6894
Epoch 1/1... Discriminator Loss: 1.3132... Generator Loss: 0.5887
Epoch 1/1... Discriminator Loss: 1.0774... Generator Loss: 0.8991
Epoch 1/1... Discriminator Loss: 1.3274... Generator Loss: 0.6366
Epoch 1/1... Discriminator Loss: 1.4988... Generator Loss: 0.4910
Epoch 1/1... Discriminator Loss: 1.1982... Generator Loss: 0.8400
Epoch 1/1... Discriminator Loss: 1.1812... Generator Loss: 0.7307
Epoch 1/1... Discriminator Loss: 1.2129... Generator Loss: 0.6750
Epoch 1/1... Discriminator Loss: 1.5055... Generator Loss: 0.5893
Epoch 1/1... Discriminator Loss: 1.3938... Generator Loss: 0.7116
Epoch 1/1... Discriminator Loss: 1.2875... Generator Loss: 0.7408
Epoch 1/1... Discriminator Loss: 1.3643... Generator Loss: 0.6073
Epoch 1/1... Discriminator Loss: 1.2870... Generator Loss: 0.5791
Epoch 1/1... Discriminator Loss: 1.4081... Generator Loss: 0.4733
Epoch 1/1... Discriminator Loss: 1.2993... Generator Loss: 1.0001
Epoch 1/1... Discriminator Loss: 1.3364... Generator Loss: 0.6397
Epoch 1/1... Discriminator Loss: 1.2499... Generator Loss: 0.6855
Epoch 1/1... Discriminator Loss: 1.2348... Generator Loss: 0.7490
Epoch 1/1... Discriminator Loss: 1.5250... Generator Loss: 0.6034
Epoch 1/1... Discriminator Loss: 1.2655... Generator Loss: 0.6737
Epoch 1/1... Discriminator Loss: 1.5945... Generator Loss: 0.5627
Epoch 1/1... Discriminator Loss: 1.2615... Generator Loss: 0.6929
Epoch 1/1... Discriminator Loss: 1.4647... Generator Loss: 0.4387
Epoch 1/1... Discriminator Loss: 1.1851... Generator Loss: 0.8493
Epoch 1/1... Discriminator Loss: 1.3986... Generator Loss: 0.8144
Epoch 1/1... Discriminator Loss: 1.3922... Generator Loss: 0.6597
Epoch 1/1... Discriminator Loss: 1.2148... Generator Loss: 0.7078
Epoch 1/1... Discriminator Loss: 1.3041... Generator Loss: 0.7494
Epoch 1/1... Discriminator Loss: 1.3532... Generator Loss: 0.6225
Epoch 1/1... Discriminator Loss: 1.4161... Generator Loss: 0.6740
Epoch 1/1... Discriminator Loss: 1.5046... Generator Loss: 0.6025
Epoch 1/1... Discriminator Loss: 1.3128... Generator Loss: 0.6997
Epoch 1/1... Discriminator Loss: 1.3213... Generator Loss: 0.6229
Epoch 1/1... Discriminator Loss: 1.3569... Generator Loss: 0.6237
Epoch 1/1... Discriminator Loss: 1.3995... Generator Loss: 0.6665
Epoch 1/1... Discriminator Loss: 1.4798... Generator Loss: 0.5765
Epoch 1/1... Discriminator Loss: 1.3674... Generator Loss: 0.7324
Epoch 1/1... Discriminator Loss: 1.3631... Generator Loss: 0.6055
Epoch 1/1... Discriminator Loss: 1.4755... Generator Loss: 0.5553
Epoch 1/1... Discriminator Loss: 1.3486... Generator Loss: 0.7235
Epoch 1/1... Discriminator Loss: 1.5355... Generator Loss: 0.5539
Epoch 1/1... Discriminator Loss: 1.2481... Generator Loss: 0.6812
Epoch 1/1... Discriminator Loss: 1.4404... Generator Loss: 0.4887
Epoch 1/1... Discriminator Loss: 1.4697... Generator Loss: 0.5836
Epoch 1/1... Discriminator Loss: 1.3218... Generator Loss: 0.6096
Epoch 1/1... Discriminator Loss: 1.3390... Generator Loss: 0.7008
Epoch 1/1... Discriminator Loss: 1.3127... Generator Loss: 0.6557
Epoch 1/1... Discriminator Loss: 1.3940... Generator Loss: 0.5927
Epoch 1/1... Discriminator Loss: 1.3183... Generator Loss: 0.6120
Epoch 1/1... Discriminator Loss: 1.3141... Generator Loss: 0.7317
Epoch 1/1... Discriminator Loss: 1.4773... Generator Loss: 0.6735
Epoch 1/1... Discriminator Loss: 1.2013... Generator Loss: 0.6559
Epoch 1/1... Discriminator Loss: 1.2856... Generator Loss: 0.6600
Epoch 1/1... Discriminator Loss: 1.3174... Generator Loss: 0.6276
Epoch 1/1... Discriminator Loss: 1.4535... Generator Loss: 0.5974
Epoch 1/1... Discriminator Loss: 1.3792... Generator Loss: 0.6006
Epoch 1/1... Discriminator Loss: 1.3832... Generator Loss: 0.8109
Epoch 1/1... Discriminator Loss: 1.3142... Generator Loss: 0.6684
Epoch 1/1... Discriminator Loss: 1.5550... Generator Loss: 0.5008
Epoch 1/1... Discriminator Loss: 1.2635... Generator Loss: 0.6463
Epoch 1/1... Discriminator Loss: 1.3460... Generator Loss: 0.6922
Epoch 1/1... Discriminator Loss: 1.1818... Generator Loss: 0.7414
Epoch 1/1... Discriminator Loss: 1.5740... Generator Loss: 0.5006
Epoch 1/1... Discriminator Loss: 1.4031... Generator Loss: 0.6925
Epoch 1/1... Discriminator Loss: 1.4099... Generator Loss: 0.6119
Epoch 1/1... Discriminator Loss: 1.4362... Generator Loss: 0.5901
Epoch 1/1... Discriminator Loss: 1.3595... Generator Loss: 0.5620
Epoch 1/1... Discriminator Loss: 1.2786... Generator Loss: 0.6666
Epoch 1/1... Discriminator Loss: 1.3845... Generator Loss: 0.6731
Epoch 1/1... Discriminator Loss: 1.2299... Generator Loss: 0.7992
Epoch 1/1... Discriminator Loss: 1.5848... Generator Loss: 0.7219
Epoch 1/1... Discriminator Loss: 1.2886... Generator Loss: 0.6666
Epoch 1/1... Discriminator Loss: 1.4121... Generator Loss: 0.5912
Epoch 1/1... Discriminator Loss: 1.4013... Generator Loss: 0.6308
Epoch 1/1... Discriminator Loss: 1.2767... Generator Loss: 0.6386
Epoch 1/1... Discriminator Loss: 1.3424... Generator Loss: 0.6219
Epoch 1/1... Discriminator Loss: 1.2901... Generator Loss: 0.6354
Epoch 1/1... Discriminator Loss: 1.5105... Generator Loss: 0.5979
Epoch 1/1... Discriminator Loss: 1.4594... Generator Loss: 0.6376
Epoch 1/1... Discriminator Loss: 1.3419... Generator Loss: 0.6720
Epoch 1/1... Discriminator Loss: 1.2911... Generator Loss: 0.6273
Epoch 1/1... Discriminator Loss: 1.3824... Generator Loss: 0.6657
Epoch 1/1... Discriminator Loss: 1.4743... Generator Loss: 0.6564
Epoch 1/1... Discriminator Loss: 1.2991... Generator Loss: 0.6150
Epoch 1/1... Discriminator Loss: 1.4819... Generator Loss: 0.5470

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.